Predicting pork quality using Vis/NIR spectroscopy

Balage, Juliana Monteiro; Silva, Saulo da Luz e; Gomide, Catarina Abdalla; Bonin, Marina de Nadai; Figueira, Ana Cristina

doi:10.1016/j.meatsci.2015.04.018

Cited by 77 publications

(60 citation statements)

References 35 publications

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“…Those values were similar to the R 2 of 0.82 and RPD of 2.6 reported by Balage et al (2015) for a*, also considered low by those authors. The prediction equation for L* also yielded R 2 and RPD values ( Table 2) that were unsatisfactory, and similar to those reported by Kapper et al (2012b), measuring 685 pork LD samples from four slaughterhouses (R 2 = 0.70 and RPD = 1.82).…”

Section: Resultssupporting

confidence: 91%

Prediction of pH and color in pork meat using VIS-NIR Near-infrared Spectroscopy (NIRS)

et al. 2019

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The potential of near-infrared spectroscopy (NORS) to predict the physicochemical characteristics of the porcine longissimus dorsi (LD) muscle was evaluated in comparison to the standard methods of pH and color for meat quality analysis compared to the pH results with Colorimeter and pH meter. Spectral information from each sample (n = 77) was obtained as the average of 32 successive scans acquired over a spectral range from 400 -2498 nm with a 2 -nm gap for calibration and validation models. Partial least squares (PLS) regression was used for each individual model. An R 2 and a residual predictive deviation (RPD) of 0.67/1.7, 0.86/2, and 0.76/1.9 were estimated for color parameters L*, a*, and b*, respectively. Final pH had an R 2 of 0.67 and a RPD of 1.6. NORS showed great potential to predict color parameter a* of porcine LD muscle. Further studies with larger samples should help improve model quality.Keywords: carcasses; color analysis; longissimus dorsi; partial least squares regression; pH analysis.Practical Application: Development of prediction curves for evaluation of pork meat quality.

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Section: Resultssupporting

confidence: 91%

Prediction of pH and color in pork meat using VIS-NIR Near-infrared Spectroscopy (NIRS)

et al. 2019

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“…29 Due to the lack of homogeneity, repetitive scans were not totally replicated in a single sample; increasing the scans per sample or using a larger surface area for each scan could also help to improve precision. These results agree with Prieto et al 31 and Balage et al, 32 who reported low NIR spectroscopy predictability for intramuscular fat content in beef and pork, respectively, when spectra were collected on intact muscle using fiber optic devices from 350-1800 nm and 400-1395 nm, respectively.…”

Section: Prediction Of Chemical Componentssupporting

confidence: 90%

“…Nevertheless, NIR spectra collected from intact samples also did not yield accurate predictions in beef, pig, and poultry meat. 32,49,50,54 Shear force measures obtained from the same muscle can have a high variability due to muscle heterogeneity, thus making NIR spectroscopy prediction for this parameter difficult. Nevertheless, Balage et al 32 observed a classification model that correctly categorized 72% of pork samples into tender and tough classes using Vis-NIR spectroscopy.…”

Section: Calibration and Prediction Performancementioning

confidence: 99%

“…32,49,50,54 Shear force measures obtained from the same muscle can have a high variability due to muscle heterogeneity, thus making NIR spectroscopy prediction for this parameter difficult. Nevertheless, Balage et al 32 observed a classification model that correctly categorized 72% of pork samples into tender and tough classes using Vis-NIR spectroscopy. These authors stated that a technology able to discriminate tender pork with such accuracy might be useful for industrial applications, as it could allow the high-quality meat to be rapidly identified for differentiated product lines.…”

Section: Calibration and Prediction Performancementioning

confidence: 99%

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A Review of the Principles and Applications of Near-Infrared Spectroscopy to Characterize Meat, Fat, and Meat Products

et al. 2017

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Consumer demand for quality and healthfulness has led to a higher need for quality assurance in meat production. This requirement has increased interest in near-infrared (NIR) spectroscopy due to the ability for rapid, environmentally friendly, and noninvasive prediction of meat quality or authentication of added-value meat products. This review includes the principles of NIR spectroscopy, pre-processing methods, and multivariate analyses used for quantitative and qualitative purposes in the meat sector. Recent advances in portable NIR spectrometers that enable new online applications in the meat industry are shown and their performance evaluated. Discrepancies between published studies and potential sources of variability are discussed, and further research is encouraged to face the challenges of using NIRS technology in commercial applications, so that its full potential can be achieved.

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“…1 In the food industry it is used, amongst other applications, for monitoring meat quality and analysis of meat composition (muscle, intramuscular fat, subcutaneous fat, water etc.). [2][3][4] Due to its non-invasiveness, HSI in the visible and near infrared (NIR) spectral range from 500 nm to 1000 nm has been proved to provide a large amount of relevant information about physiological parameters (e.g. blood volume, tissue oxygenation, water content) in different medical application areas like diabetic foot and skin ulcers, [5][6][7] tissue perfusion measurements, wound analysis and flap monitoring.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging of the degradation of meat and comparison with necrotic tissue in human wounds

Holmer

Hornberger

Wild

et al. 2019

J. Spectral Imaging

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The objective evaluation of scattering tissue and the discrimination of tissue types is an issue that cannot be solved with colour cameras and image processing alone in many cases. Examples can be found in the determination of freshness and ageing of meat, and the discrimination of tissue types in food technology. In medical applications tissue discrimination is also an issue, e.g. in wound diagnostics. A novel hyperspectral imaging setup with powerful signal analysis algorithms is presented which is capable of addressing these topics. The spectral approach allows the chemical analysis of material and tissues and the measurement of their temporal change. We present a method of hyperspectral imaging in the visible-near infrared range which allows both the separation and spatial allocation of different tissue types in a sample, as well as the temporal changes of the tissue as an effect of ageing. To prove the capability of the method, the ageing of meat (slices of pork) was measured and, as a medical example, the application of the hyperspectral imaging setup for the recording of wound tissue is presented. The method shows the ability to discriminate the different tissue components of pork meat, and the ageing of the meat is observable as changes in spectral features. An additional result of our study is the fact that some spectral features, which seem to be typical for the ageing of the meat, are similar to those observed in the necrotic tissue from wound diagnostics in medicine.

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Predicting pork quality using Vis/NIR spectroscopy

Cited by 77 publications

References 35 publications

Prediction of pH and color in pork meat using VIS-NIR Near-infrared Spectroscopy (NIRS)

Prediction of pH and color in pork meat using VIS-NIR Near-infrared Spectroscopy (NIRS)

A Review of the Principles and Applications of Near-Infrared Spectroscopy to Characterize Meat, Fat, and Meat Products

Hyperspectral imaging of the degradation of meat and comparison with necrotic tissue in human wounds

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